ByoTrack is a Python library that enables tracking of biological object in videos (2D or 3D).
Many bioimage informatics tools already implement their own tracking tools (Icy [1], ImageJ [6], ...) but most of them are implemented in Java which makes it difficult for non-Java developers to experiment with the code. It is also difficult to integrate deep learning algorithms (mainly developed in Python) into these software.
We provide a unified python API for tracking that can be easily extended with new (and old) algorithms. We also provide implementations of well-known algorithms following our API. ByoTrack is based on numpy, pytorch and numba allowing fast computations with the access to the full python ecosystem.
Overview:
$ pip install byotrack
Some tracker implementations require additional dependencies that are not installed with the library, to use them you need to install their dependencies on your own. Here is the complete list:
For visualization, with byotrack.visualize module you need to install matplotlib.
import byotrack
# Load some specific implementations
from byotrack.implementation.detector.wavelet import WaveletDetector
from byotrack.implementation.linker.icy_emht import IcyEMHTLinker
from byotrack.implementation.refiner.cleaner import Cleaner
from byotrack.implementation.refiner.stitching import EMC2Stitcher
# Read a video from a path, normalize and aggregate channels
video = byotrack.Video(video_path)
transform_config = VideoTransformConfig(aggregate=True, normalize=True, q_min=0.01, q_max=0.999)
video.set_transform(transform_config)
# Create a multi step tracker
## First the detector
## Smaller scale <=> search for smaller spots
## The noise threshold is linear with k. If you increase it, you will retrieve less spots.
detector = WaveletDetector(scale=1, k=3.0, min_area=5)
## Second the linker
## Hyperparameters are automatically chosen by Icy
linker = IcyEMHTLinker(icy_path)
## Finally refiners
## If needed you can add Cleaning and Stitching operations
refiners = []
if True:
refiners.append(Cleaner(5, 3.5)) # Split tracks on position jumps and drop small ones
refiners.append(EMC2Stitcher()) # Merge tracks if they track the same particle
tracker = byotrack.MultiStepTracker(detector, linker, refiners)
# Run the tracker
tracks = tracker.run(video)
# Save tracks
byotrack.Track.save(tracks, output_path)
Please refer to the official documentation.
In coming...
@article{hanson2024automatic,
title={Automatic monitoring of neural activity with single-cell resolution in behaving Hydra},
author={Hanson, Alison and Reme, Raphael and Telerman, Noah and Yamamoto, Wataru and Olivo-Marin, Jean-Christophe and Lagache, Thibault and Yuste, Rafael},
journal={Scientific Reports},
volume={14},
number={1},
pages={5083},
year={2024},
publisher={Nature Publishing Group UK London}
}
FAQs
Biological particle tracking with Python
We found that byotrack demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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